Machine learning approach for detecting Covid-19 from speech signal using Mel frequency magnitude coefficient.

The Covid-19 pandemic is one of the most significant global health concerns that have emerged in this decade. Intelligent healthcare technology and techniques based on speech signal and artificial intelligence make it feasible to provide a faster and more efficient timely detection of Covid-19. The main objective of our study is to design speech signal-based noninvasive, low-cost, remote diagnosis of Covid-19. In this study, we have developed system to detect Covid-19 from speech signal using Mel frequency magnitude coefficients (MFMC) and machine learning techniques. In order to capture higher-order spectral features, the spectrum is divided into a larger number of subbands with narrower bandwidths as MFMC, which leads to better frequency resolution and less overall noise. As a consequence of an improvement in frequency resolution as well as a decrease in the quantity of noise that is included with the extraction of MFMC, the higher-order MFMCs are able to identify Covid-19 from speech signals with an increased level of accuracy. The procedures for machine learning are often less complicated than those for deep learning, and they may commonly be carried out on regular computers. However, deep learning systems need extensive computing power and data storage. Twelve, twenty-four, thirty, and forty spectral coefficients are obtained using MFMC in our study, and from these coefficients, performance is accessed using machine learning classifiers, such as random forests and K-nearest neighbor (KNN); however, KNN has performed better than the other model with having AUC score of 0.80.

Comparative Analysis of COVID 19 Detection from Cough Speech Using Machine Learning Classifiers

Corona Virus Disease-2019, or COVID-19, has been on the rise since its emergence, so its early detection is necessary to stop it from spreading rapidly. Speech detection is one of the best ways to detect it at an early stage as it exhibits variations in the nasopharyngeal cavity and can be performed ubiquitously. In this research, three standard databases are used for detection of COVID-19 from speech signal. The feature set includes the baseline perceptual features such as spectral centroid, spectral crest, spectral decrease, spectral entropy, spectral flatness, spectral flux, spectral kurtosis, spectral roll off point, spectral skewness, spectral slope, spectral spread, harmonic to noise ratio, and pitch. 05 ML based classification techniques have been employed using these features. It has been observed that Generalized Additive Model (GAM) classifier offers an average of 95% and a maximum of 97.55% accuracy for COVID-19 detection from cough signals. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Depression detection based on linear and nonlinear speech features in I-vector/SVDA framework.

This study proposes depression detection systems based on the i-vector framework for classifying speakers as depressed or healthy and predicting depression levels according to the Beck Depression Inventory-II (BDI-II). Linear and non-linear speech features are investigated as front-end features to i-vectors. To take advantage of the complementary effects of features, i-vector systems based on linear and non-linear features are combined through the decision-level fusion. Variability compensation techniques, such as Linear Discriminant Analysis (LDA) and Within-Class Covariance Normalization (WCCN), are widely used to reduce unwanted variabilities. A more generalizable technique than the LDA is required when limited training data are available. We employ a support vector discriminant analysis (SVDA) technique that uses the boundary of classes to find discriminatory directions to address this problem. Experiments conducted on the 2014 Audio-Visual Emotion Challenge and Workshop (AVEC 2014) depression database indicate that the best accuracy improvement obtained using SVDA is about 15.15% compared to the uncompensated i-vectors. In all cases, experimental results confirm that the decision-level fusion of i-vector systems based on three feature sets, TEO-CB-Auto-Env+Δ, Glottal+Δ, and MFCC+Δ+ΔΔ, achieves the best results. This fusion significantly improves classifying results, yielding an accuracy of 90%. The combination of SVDA-transformed BDI-II score prediction systems based on these three feature sets achieved RMSE and MAE of 8.899 and 6.991, respectively, which means 29.18% and 30.34% improvements in RMSE and MAE, respectively, over the baseline system on the test partition. Furthermore, this proposed combination outperforms other audio-based studies available in the literature using the AVEC 2014 database.